Imaging and tracking systems typically include sensors to identify and track objects. For example, some sensors, such as radar systems, send out signals that reflect from objects and are received by the system. Other sensors, such as electro-optical sensors, receive electromagnetic radiation signals from the objects themselves. Improvements in this field have been directed to refining these sensors to be more accurate.
In particular, electro-optical sensors typically use telescopes and focal plane arrays that detect infrared (IR) radiation. Many approaches for autonomous optical detection in environments with clutter depend on combinations of signature enhancement, shape matching and motion detection. These approaches try to exploit differences between an object of interest and background that are presented under the given conditions and scenarios. For example, large temperature differentials between the atmosphere, terrestrial background and the object may allow adaptive thresholding on consecutive detector output arrays to produce consistent exceedances which can then be correlated into high-probability target detections. Performance trades result in requirements for detection and false-alarm-rate probabilities versus sensor hardware, processor architectures and data analysis algorithms.
In the case of space-to-ground surveillance, specific IR bands are employed to optimally match known target signature responses while suppressing those of the background. In some cases, multiple bands are used to take advantage of the difference in responses between them, thus creating multi-channel data sets upon which to discriminate objects of interest from unwanted background clutter.
Cost-effective, surveillance systems looking for small (unresolved), dim (background limited) targets at low-earth orbit ranges require uninterrupted scan processing at frame rates dictated by target signal integration, maximum background radiance levels and mission timelines. Requirements are generally driven by line of sight (LOS) rates and projected pixel sizes, or ground sample distance (GSD). Detection limiting effects associated with these types of systems involve static field-of-view patterns, or fixed pattern noise (FPN), temporal noise (e.g., thermal, photon-shot, digital readout, etc.) and detector pixel imperfections (inoperable pixels) in the IR sensor and suppression of the local background clutter structure that competes with the target signature.
In existing approaches, removing FPN requires a calibration step in which the current pattern is estimated. This usually involves the generation of a “dark frame” in the absence of input producing a frame in which only the FPN component is present. Since FPN patterns can drift over time scales larger than detection processing interval, periodic FPN calibration is required. In space applications, dark frames can be generated by directing the sensor LOS toward empty space and collecting frame sets which are then averaged into an estimate of the current FPN. This process has the effect of impacting timeline continuity, and maneuvering resources.
Temporal noise in the detector is often dealt with by frame integration which tends to average out the noise while strengthening the relatively constant target signature. Effective frame integration requires a registration process that correlates constant background patterns. The registration process can be based on image frame cross-correlation, or by navigation unit data. Integration intervals (and hence the number of frame additions) are set so that the target motion over the period keeps it within a registered pixel where its signature can be accumulated. The result will be a target-plus-background image frame in which pixel noise has been reduced.
The target signature must now be isolated from the background pattern. This is done by subtracting a local estimate of the background as it would appear in the same registered and integrated frame in the absence of the target. In a practical sense, a second integrated frame is generated at a slightly different time where the targets have slightly shifted their positions into different pixels. A difference frame now indicates the presence of moving objects as co-located doublet pulses surrounded by residual background clutter.
The key to detection performance is suppression of the residual clutter to a point that produces Signal-to-Noise Ratios (SNR) suitable for detection and false alarm rate requirements.